 # Higher-Order Java Parallelism, Part 2: Parallel List Transformations

With a Callable monad and Parallel Strategies in hand, we’re ready to construct some general-purpose parallel functions of a higher order.

Something we’ll want to do quite often in parallel programming is run a function over a whole list of values, in parallel. So often, in fact, that it would be very tedious and repetitive to write a loop every time we want to do this, sparking off threads for each function call, and collecting the results. Instead, what we’re going to develop next is a kind of parallel list functor. That is, a higher-order function that will turn any function into a function that operates on every element of a list simultaneously.

First, let’s get some terminology out of the way, since I’ve been accused of not defining my terms.

A higher-order function is simply a function that takes a function as an argument.

A functor is any type T for which there exists a higher-order function, call it fmap, that transforms a function of type F<A,B> into a function F<T<A>,T<B>>. This fmap function must also obey the laws of identity and composition such that the following expressions return true for all x, p, and q:

```  fmap(identity()).f(x) == x
fmap(compose(p, q)).f(x) == fmap(p).f(fmap(q).f(x))```

Here, identity is a first-class function of type F<A,A> that just returns its argument, and compose is function composition. Composition is defined to mean that compose(p, q).f(x) is the same as p.f(q.f(x)).

For example, Functional Java’s List class is a functor, because it comes equipped with a method List<B> map(F<A,B> f) which obeys the laws above. The implementation of List’s map method is trivial: It iterates over the list, calling the given function for each element, and returns the list of the results.

There’s a lot of information about functors and monads on the Internet, and I’ve written about them before, with examples in Java. Tony Morris also wrote a great post where he explains functors as “function application in an environment”. That’s a good way of looking at things.

I also want to make it clear, if you’re just now tuning in, that I’m using classes from Functional Java in the code examples, in addition to classes from the J2SE concurrency library. Basically only Callable and Future are from the J2SE library. Strategy, List, and the Callables utility class are from Functional Java. The code used here is from the latest trunk head revision of that library and may differ from the current official release.

Now, let’s get to the money.

Mapping Over a List in Parallel

We’re going to make a very similar kind of thing to a list functor, except it’s going to map a function over a list in parallel. Our parallel list transformation will take the form of a static method called parMap. It will take any existing function and turn it into a parallel function on lists. That is, if we already have written a function that takes an Integer and returns a String, we can pass that function to parMap, and it will return us a function that takes a list of Integers and returns a list of Strings. This means that nothing has to be rewritten or refactored to support the mapping of a function over a list in parallel. The parMap function will convert our existing code to parallel code, at runtime.

Here’s parMap:

```  public <B> F<List<B>, Callable<List<A>>> parMap(final F<B, A> f) {
return new F<List<B>, Callable<List<A>>>() {
public Callable<List<A>> f(final List<B> as) {
return sequence(as.map(compose(par(), callable(f))));
}
};
}```

That’s a lot of power for so little code. Let’s read through what it does. It takes a function f and returns a new function that takes a list called as. This new function transforms f with Callables.callable to wrap its return type in a callable (or: callable(f) applies the Kleisli arrow for Callables to f), and composes that with a function called par(). It then maps the resulting composition over the list as, which will result in a list of Callables, which we turn into a Callable of a List with the sequence function. The only new thing here is par(), which is a very simple instance method on Strategy:

```  public F<Callable<A>, Callable<A>> par() {
return compose(Strategy.<A>obtain(), f());
}```

So par() is just the Strategy’s function composed with the obtain() method that we created in Part 1. This turns the Future back into a Callable so that we can manipulate it in a lazy manner. This little guy is the meat of parMap. That is, parMap is basically just mapping par() over a list.

The result of parMap(myFun).f(myList), then, is a Callable of a list that, when called, will give us the results of calling myFun on every element of myList in parallel. What’s more, it will work for any kind of parallelisation Strategy, and it will return immediately (remember, Callable is lazy), ready for us to make further calculations on the results even while those results are being calculated.

I think that’s pretty cool.

Nested Parallelism

Sometimes the problem at hand is not quite so flat that it can be solved with just a map over a list. You might have a need for nested traversal of lists, where for every element in a list you traverse another list. We can model that behaviour with a higher-order function very similar to parMap, except that it takes a function that generates a list. This higher-order function is parFlatMap:

```  public static <A, B> Callable<List<B>> parFlatMap(final Strategy<List<B>> s,
final F<A, List<B>> f,
final List<A> as) {
return fmap(List.<B>join_()).f(s.parMap(f).f(as));
}```

In this definition, fmap is from the Callable monad, as described in Part 1. Notice that parFlatMap provides an example use of parMap, using it to turn the given function into a parallel function. The call to parMap with that function will actually yield a Callable<List<List<B>>> (can you see why?), so we use a final join lifted into the Callable environment with fmap, to turn the List of Lists into just a List.

The parFlatMap function is analogous to a parallel list monad, and it works very much like a parallel version of nested loops. For example, given a list of Points pts, a distance function dist that takes two Points and returns the distance between them, and a Strategy<Double> s, we can use parFlatMap to calculate the distance between every Point and every other, in parallel:

```  Callable<List<Double>> distances = parFlatMap(s, new F<Point, List<Double>>() {
public List<Double> f(final Point p) {
return pts.map(dist.f(p));
}
}, pts);```

The inner “loop” is represented by List.map, so we’re traversing the list serially, multiplying every element in parallel with each element in sequence. I.e. the outer loop is parallel and the inner loop is serial. It’s quite possible to replace map with parMap, and I’ll leave that as an exercise for the reader (hint: you will need both fmap and join). Note that the call to parFlatMap will return immediately, and the computation will be carried out in the background.

Position-Wise Parallelism and Concurrent Folding

Another function I want to talk about is the parZipWith function. By contrast to parFlatMap, it is like a parallel version of a single loop over two lists. Values from each list are taken at matching positions and the pairs are fed through the argument function all at the same time:

```  public static <A, B, C> F2<List<A>, List<B>, Callable<List<C>>> parZipWith(final Strategy<C> s,
final F2<A, B, C> f) {
return new F2<List<A>, List<B>, Callable<List<C>>>() {
public Callable<List<C>> f(final List<A> as, final List<B> bs) {
return sequence(as.zipWith(bs, compose(Callables.<B, C>arrow(), curry(f))).map(s.par()));
}
};
}```

You will note that parZipWith simply uses zipWith from Functional Java’s List class. There’s some manouvering required to turn the argument function into the right form. The curry method turns the F2<A,B,C> into the equivalent F<A,F<B,C>> (sometimes called a Curried function). The arrow function is the first-class Kleisli arrow for Callables. That just means that composing it with f yields a version of f which returns a Callable.

Let’s take an example of using parZipWith to do useful work. If we had a list of Points representing a path, and we wanted to get the total length of the path, we could zip the list and its tail (every element except the first) with the dist function and take the sum of the results:

`  Double length = parZipWith(s, dist).f(pts, pts.tail()).call().foldLeft(curry(sum), 0);`

For brevity’s sake, I’m pretending that there’s already a function called sum, which is just an F2<Double, Double, Double> that returns the sum of its two arguments. If foldLeft is something new to you, then have a look at one implementation of it. It’s a more general abstraction of a for-loop than map() is. Folding a list {1,2,3} with the sum function (and 0) can be thought of as replacing all the commas with plusses to yield [0+1+2+3]. We could have just as well iterated over the list, adding each value to a total, instead of using a foldLeft function. But we didn’t, for a reason that will become apparent presently.

There’s something about the above example that’s dissatisfying. We’re getting the distances in parallel, but then call() sticks out like a sore thumb. The computation will block until the list of distances is ready, then it will fold the list with the sum function. What’s worse, call() might throw an Exception. There’s a way we can avoid all that by traversing the list of distances while they’re being calculated. What we need to do is somehow lift the foldLeft function into the Callable environment. The call to foldLeft above turns a List<Double> into a Double. What we want is to avoid the call to call(), and turn our Callable<List<Double>> into a Callable<Double> directly.

This is really easy to do, and it’s a good thing we’re using a folding function rather than a for-loop. Here’s what we do:

```  Callable<List<Double>> edges = parZipWith(s, dist).f(pts, pts.tail());
Callable<Double> length = par(fmap(List.<B, B>foldLeft().f(curry(sum)).f(0)).f(edges));```

There. No exceptions, and no waiting for results. This code will return immediately and perform the whole computation in the background. The part that calculates the edge lengths is the same as before, and we’re keeping that in a variable edges for clarity. The other line may require some explanation. We’re getting a first-class foldLeft function from the List class, partially applying it to yield a function that folds a List<Double> into a Double. Then we’re promoting it with fmap into the Callable monad. The promoted function then gets applied to edges, and finally the whole fold is evaluated concurrently by passing it to par.

Hopefully you can see what an immensely powerful tool for parallel programming functional style and higher-order functions are, even in Java. We were able to develop a couple of one-liners above that are terser, clearer, more maintainable, more re-usable (and more type-safe) than anything we could write in imperative object-oriented style using producer/consumer, factories, inversion of control, etc. There’s no secret sauce, no special syntax, no functional fairy dust. It’s just plain old Java with a handful of very useful interfaces.

In the third installment of this series, we will develop a tiny light-weight library for threadless Actors that can juggle millions of simultaneous computations in only a few threads. Stay close.

Note: The code herein is adapted from a Haskell library by Phil Trinder, Hans-Wolfgang Loidl, Kevin Hammond et al.